36 citations · 39 across the 14 of their papers we have counts for
10 papers · 1 filter
The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning
Ameya D. Jagtap
Automatic differentiation (AD) lets neural networks compute derivatives of governing equations to machine precision, and this precision has made it the computational backbone of ph…
GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model
Trishit Mondal, Prajwal Bharadwaj, Nikhil Karanjgaokar +1
Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and can only be inferred indirectl…
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Sidharth S. Menon, Irina Tezaur, Ameya D. Jagtap
Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability while solving partial differen…
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
Sidharth S. Menon, Ameya D. Jagtap
Kolmogorov-Arnold Networks (KANs) have recently emerged as a compelling alternative to multilayer perceptrons, offering enhanced interpretability via functional decomposition. Howe…
In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes
Trishit Mondal, Ameya D. Jagtap
Transformer architectures have revolutionized machine learning across a wide range of domains, from natural language processing to scientific computing. However, their growing depl…
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
Trishit Mondal, Ameya D. Jagtap
The hypersonic unstart phenomenon poses a major challenge to reliable air-breathing propulsion at Mach 5 and above, where strong shock-boundary-layer interactions and rapid pressur…
BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics
Yunhao Zhang, Sidharth S. Menon, Lin Cheng +2
In this work, we employ physics-informed neural operators to map pressure profiles from an input function space to the corresponding bubble radius responses. Our approach employs a…
An Approximation Theory Perspective on Machine Learning
Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
A central problem in machine learning is often formulated as follows: Given a dataset , which is a sample drawn from an unknown probability distribution, th…
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs
Sidharth S. Menon, Ameya D. Jagtap
High-dimensional partial differential equations (PDEs) arise in diverse scientific and engineering applications but remain computationally intractable due to the curse of dimension…
RiemannONets: Interpretable Neural Operators for Riemann Problems
Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap +1
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numeri…